| Despite recent technological advances in the area of radar hydrology, reliable quantification of radar-rainfall amounts on scales relevant to hydrological applications is not fully realized. A major problem is the lack of accurate estimation of uncertainty levels of radar-rainfall products. Discrepancies between radar and rain gauge rainfall quantities—traditionally considered as an approximation of the true ground rainfall—are mainly attributed to two main factors: rainfall small-scale natural variability and differences in sampling properties between radar and gauge. The present research addresses these issues in an attempt to develop sound validation methodologies for radar-rainfall products. In the framework of this study, some fundamental questions are posed: How is rainfall variable over scales smaller than the radar-resolved scales? What role does such variability play in the evaluation of radar-rainfall estimates? Is there a radarspace/gauge-time equivalence where the observations of both sensors are possibly comparable?; This study makes use of extensive experimental rainfall multi-sensor observations collected during the field campaigns of the NASA's Tropical Rainfall Measuring Mission (TRMM) in addition to other experimental datasets. First, errors associated with gauge measurements—as the main independent data source for validation—are investigated and characterized. Then, extensive data analysis is performed to characterize rainfall variability over scales relevant to radar spatial/temporal resolutions. A statistical procedure is applied to quantify the contribution of rainfall natural variability to the significant discrepancy usually observed in radar-gauge comparisons. This eventually can be used in establishing an error budget for radar-rainfall products. Finally, a non-parametric statistical procedure is developed and applied in an effort to establish a comprehensive validation framework. A main element is a transformation model of point-to-area rainfall which is experimentally verified. The findings of this research have implications for the use of radar-rainfall data in applications such as hydrologic modeling, flood forecasting, numerical weather prediction models, and validation of other remote sensing rainfall estimates, among others. |
